Resources

A comprehensive platform that links research papers with open-source implementations, providing access to state-of-the-art models and performance benchmarks across various fields in ML, CV, DL, and NLP.

A leader in NLP research, Hugging Face provides an extensive library of pretrained models for NLP tasks, including transformers, and also offers datasets and tools for model deployment.

A repository of pretrained machine learning models ready to be used for tasks like image classification, object detection, text embeddings, and more, optimized for TensorFlow.

A hub for pretrained PyTorch models, offering easy access to research models for various tasks, including CV and NLP.

A set of popular deep learning models pretrained on ImageNet, available in Keras for tasks such as image classification and feature extraction.

Provides access to powerful language models like GPT for NLP tasks, along with API services for generating text, performing sentiment analysis, and more.

A massive repository of datasets across various domains, with community-driven resources, and code examples for analysis and model development.

A high-level deep learning library built on PyTorch, offering tools for training deep learning models quickly and efficiently, with an emphasis on ease of use.

An open-source computer vision library providing tools and modules for image and video analysis, frequently used in CV research.

A platform for data labeling and annotation, designed to accelerate the training of CV and NLP models by providing high-quality labeled datasets.

An NLP library in Python that offers fast, efficient, and pretrained models for tasks like tokenization, named entity recognition, and text classification.

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

A comprehensive series of courses on deep learning covering neural networks, CNNs, RNNs, and more, with practical programming assignments in Python. Great for learners moving into DL.

A 4-course series that covers modern NLP topics like text classification, sentiment analysis, and sequence models using TensorFlow.

Introduces key concepts in computer vision, including feature extraction and image filtering, through a series of engaging programming assignments.

A series of courses focusing on using TensorFlow for advanced computer vision tasks, including GANs, object detection, and image segmentation.